Triple
T19755064
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anathem |
E474480
|
entity |
| Predicate | hasNeologisms |
P70080
|
FINISHED |
| Object | yes |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: yes | Statement: [Anathem, hasNeologisms, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNeologisms Context triple: [Anathem, hasNeologisms, yes]
-
A.
hasSociolinguisticPhenomenon
chosen
Indicates a relationship where a subject exhibits, involves, or is associated with a particular sociolinguistic phenomenon (such as dialectal variation, code-switching, or language change in social context).
-
B.
hasColloquialVariety
Indicates that one linguistic form, expression, or variety is an informal, colloquial counterpart or version of another.
-
C.
hasGlossonym
Indicates a relationship where an entity is associated with the specific name or term used to refer to a language (its glossonym).
-
D.
usesColloquialCharacters
Indicates that an expression, name, or text is written using informal, non-standard, or colloquial characters rather than formal or standard script.
-
E.
hasOrthographyProposal
Indicates that an entity is associated with a proposed system or set of rules for its written form or spelling.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8e51940a0819087bd2996f98da668 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6529dada081909c5b4d65247c6032 |
completed | April 20, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69e5305016e08190b9561a96baecb0b8 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:48 p.m.